Comparative Evaluation of Face Detection Algorithms

Yamout, Ahmed; Abdelmawgood, Ahmed; Sadick, Ebraam; Naguib, Mohamed

doi:10.1109/icenco49778.2020.9357386

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Un subcampo muy disputado de la visión por computadora es la detección de rostros, debido a su uso versátil en muchos campos, como la seguridad, el diagnóstico médico, el entretenimiento y las aplicaciones militares. A medida que se desarrolla la tecnología su objetivo es ejecutarse más rápido y con mayor precisión en dispositivos móviles y computadoras remotas [2]. Los subcampos de la inteligencia artificial (IA), como el aprendizaje automático, los sistemas basados en el conocimiento, la visión por computadora, la robótica y la optimización, se han aplicado con éxito en otras industrias para lograr una mayor rentabilidad, eficiencia, seguridad y protección [3], el aprendizaje remoto se ha convertido en una opción viable, dependiendo del aprendizaje automático para lograr sus objetivos [4].…”

Section: Introductionunclassified

Emotional state of the classroom through artificial intelligence to improve the teaching-learning process in a university

Flores,

Solis-Fonseca,

Cuba-Aguilar

et al. 2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

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The objective of this research was to develop an application that allowed facial recognition using artificial intelligence to identify the emotional state of students and thus improve the teaching-learning process within the virtual classroom at a university. The methodology used was a data science model based on convolutional neural networks that collected information from the students through facial biometric analysis using an application developed in Python where the different emotional states of the students were determined in real time during the sessions. virtual class. The results obtained show that through facial recognition it was possible to perceive various emotional states during the class session of the students who are with the camera on, even if they have a lowresolution camera in their image quality, these results are shown to the teacher globally for each emotional state to determine the situation in their classroom and thus can improve their teachinglearning strategies. It is concluded that when the teacher identifies the emotional state of his students, he can improve his classes by motivating them and this allows him to fulfill the competencies of the course, in the same way, it is concluded that the security of the information is maintained by destroying the images in real time of the participants once they have been processed and evaluated even before being shown in the final consolidated to the teacher.

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Section: Introductionunclassified